code: def __init__(
      self,
      *,
      input_dim: int,
      output_dim: int,
      time_reduction_stride: int,
      conformer_input_dim: int,
      conformer_ffn_dim: int,
      conformer_num_layers: int,
      conformer_num_heads: int,
      conformer_depthwise_conv_kernel_size: int,
      conformer_dropout: float,
  ) -> None:
      super().__init__()
      self.time_reduction = _TimeReduction(time_reduction_stride)
      self.input_linear = torch.nn.Linear(
          input_dim * time_reduction_stride, conformer_input_dim
      )
      self.conformer = Conformer(
          num_layers=conformer_num_layers,
          input_dim=conformer_input_dim,
          ffn_dim=conformer_ffn_dim,
          num_heads=conformer_num_heads,
          depthwise_conv_kernel_size=conformer_depthwise_conv_kernel_size,
          dropout=conformer_dropout,
          use_group_norm=True,
          convolution_first=True,
      )
      self.output_linear = torch.nn.Linear(
          conformer_input_dim, output_dim
      )
      self.layer_norm = torch.nn.LayerNorm(output_dim)
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 1 to create a new _ConformerEncoder object from inherited base classes: torch.nn, _Transcriber with example: obj = _ConformerEncoder()